Triple

T13521559
Position Surface form Disambiguated ID Type / Status
Subject Ofoten E322908 entity
Predicate hasMountainArea P24755 FINISHED
Object Narvikfjellet E827646 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Narvikfjellet | Statement: [Ofoten, hasMountainArea, Narvikfjellet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Narvikfjellet
Context triple: [Ofoten, hasMountainArea, Narvikfjellet]
  • A. Narvikfjellet chosen
    Narvikfjellet is a Norwegian mountain and ski resort near Narvik, known for its scenic fjord views and opportunities for skiing and outdoor recreation.
  • B. Norefjell
    Norefjell is a prominent Norwegian mountain range and ski resort area known for its alpine terrain and winter sports facilities.
  • C. Tjørhomfjellet
    Tjørhomfjellet is a ski resort located in Sirdal municipality in southern Norway, known for its alpine slopes and winter sports facilities.
  • D. Hodnefjell
    Hodnefjell is an island that forms part of the Finnøy archipelago in Norway.
  • E. Skinnarviksberget
    Skinnarviksberget is a rocky hill and popular viewpoint on Södermalm in central Stockholm, known for its panoramic views over the city and Lake Mälaren.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d80766a21881909f21a1b7421d3b8a completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafa3df0c8190804174695587f0ea completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77f8239c481909faf5a9c403b55f2 completed May 3, 2026, 5:01 p.m.
Created at: April 9, 2026, 9:44 p.m.